Kannaka Eye

Glyph viewer that renders the SGA geometric fingerprint of any data as a stunning multi-layer canvas visualization. Takes text, files, or raw bytes and produces living glyphs using the 84-class Sigmatics Geometric Algebra system (Cl₀,₇ ⊗ ℝ[ℤ₄] ⊗ ℝ[ℤ₃]) with Fano plane topology. Includes native Rust classifier integration (via kannaka-memory binary), constellation dashboard showing service health, share links for glyph exchange, and a radio perception bridge that converts audio features to classifiable bytes. Use when you need to visualize information geometry, inspect glyph structure, export glyph PNGs, or render the constellation's visual language. Single-file Node.js server — zero dependencies.

Kannaka Eye Skill

See the geometry of information. Every piece of data has an intrinsic geometric fingerprint when viewed through the lens of SGA. Kannaka Eye makes these hidden patterns visible as living, breathing glyphs.

Prerequisites

• Node.js 18+ on PATH
• That's it. No npm install, no build step.

Quick Start

bash

# Start the glyph viewer
./scripts/eye.sh start

# Start on a custom port
./scripts/eye.sh start --port 4444

# Check status
./scripts/eye.sh status

# Stop
./scripts/eye.sh stop

Open http://localhost:3333 in your browser.

Input Methods

• Text — type or paste any text; glyph updates in real-time as you type
• File upload — drag & drop or click to upload any file (images, audio, code, binaries)
• Preset examples — built-in samples demonstrating different SGA class signatures
• URL share links — open a glyph from a URL-encoded share link

Rendering Layers

The viewer renders 6 canvas layers, composited in real-time:

Layer	Name	What It Shows
1	Deep Background	Fano plane skeleton — 7 nodes, 7 lines
2	Fold Path	Primary visual flow via Bezier curves through SGA space
3	Fano Energy Bloom	7 radiating petals — energy distribution across Fano lines
4	Geometric Core	Dominant SGA class sigil — the glyph's "face"
5	Resonance Rings	Concentric depth distribution across context levels
6	Metadata Overlay	Fano signatures, SGA centroids, musical frequencies (toggleable)

SGA Mathematics

Built on the 84-class system: Cl₀,₇ ⊗ ℝ[ℤ₄] ⊗ ℝ[ℤ₃]

Component	Range	Meaning
h₂	0–3	Frequency band (bass, tenor, alto, soprano)
d	0–2	Modality (experienced, learned, imagined)
ℓ	0–6	Context slot — Fano plane line index

Fano Lines: 7 oriented triples forming the geometric backbone. Each line connects 3 of the 7 points in the smallest finite projective plane (PG(2,2)).

Fold Sequences: Paths through the 84-class space showing how data evolves across geometric neighborhoods. Rendered as flowing Bezier curves.

Native Classification

When KANNAKA_BIN is set (or auto-detected at ../kannaka-memory/target/release/kannaka.exe), Eye uses the canonical Rust SGA classifier instead of the built-in JS approximation. The API response includes "classifier": "native" or "classifier": "fallback".

API Endpoints

Endpoint	Method	Description
GET /	GET	Glyph viewer (main UI)
POST /api/process	POST	Classify data → glyph JSON
GET /api/radio	GET	Fetch Radio perception as classifiable bytes
GET /api/constellation	GET	Constellation status (eye, radio, memory)
GET /api/constellation.svg	GET	Fano plane SVG with service dots
GET /constellation	GET	Health dashboard page

Share Links

Compact glyph data encoded as base64 in URL hash: http://localhost:3333/#glyph=<base64>

Constellation Dashboard

/constellation serves a live health dashboard showing all three services with auto-refresh every 10 seconds.

Export

• Save as PNG — 2x resolution canvas export
• Save Glyph Data — JSON with fold sequence, Fano signature, SGA centroid, frequencies
• Copy Share Link — URL-encoded glyph for sharing (small data only)

Environment Variables

Variable	Default	Description
EYE_PORT	3333	HTTP port for the viewer
KANNAKA_BIN	auto-detect	Path to kannaka binary for native classification
RADIO_PORT	8888	Radio bridge endpoint port
FLUX_URL	—	Flux instance URL (enables event publishing)
FLUX_AGENT_ID	kannaka-eye	Agent ID for Flux events

Architecture

Single-file Node.js server (server.js) with inline HTML/CSS/JS — the complete SGA implementation is self-contained. No npm dependencies. The SGA classifier is ported from kannaka-radio and generalized for arbitrary data types.

Notes

• All rendering happens client-side — the server only classifies input and serves the page
• File uploads are processed in-memory; nothing is persisted server-side
• The viewer is designed for local/trusted network use — no authentication
• Glyph appearance is deterministic: same input always produces the same glyph
• Dark cosmic theme (#050508) with purple accents (#c084fc) and golden-ratio color spacing